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cjh0509code-png

video2gif

by cjh0509code-png
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Python
Hybrid

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool in the server, there is no possibility of confusing it with other operations. The tool's purpose is singular and clearly described.

    Naming Consistency5/5

    The single tool name 'video_to_gif' clearly conveys its function and uses a simple, readable convention. Since there is only one tool, naming consistency is trivially maintained.

    Tool Count3/5

    A single tool is on the low end of the typical range. For a narrowly scoped service like video-to-gif conversion, one tool may be acceptable, but it feels minimal compared to servers that include multiple related utilities.

    Completeness4/5

    The tool covers the core video-to-gif conversion workflow with options for size, fps, width, time range, and loop. Minor gaps exist, such as no support for checking video metadata or asynchronous processing, but it fulfills the server's stated purpose.

  • Average 4.5/5 across 1 of 1 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 6 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations, the description carries the burden of behavioral disclosure. It covers key runtime behavior: conversion result type, size/fps presets, maximum overrides, time-range trimming, loop semantics, and public-URL requirement. It could also mention failure modes, but the provided detail is well beyond bare minimum.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is front-loaded with the core purpose and then lists parameters in a compact, well-organized block. No filler or repetition; each line adds necessary information.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    All seven input parameters and their constraints are documented, which is strong for a 7-parameter tool with no output schema. The only minor gap is that the return mechanism is vaguely described as 'returns the image' without clarifying whether it is a file, binary, or URL.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 0%, and the description fully compensates by documenting every parameter in plain language: meaning of size presets, fps/width caps, zero defaults, start/duration/loop behavior. This is precisely the semantic value the input schema lacks.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The opening sentence clearly states the verb and resource: converting a video into a GIF and returning an image. It is specific and unambiguous, with no sibling tools to distinguish from.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    There are no siblings to contrast against, but the description gives clear usage context by requiring a publicly downloadable direct video URL and enumerating format constraints. It does not explicitly state exclusions or when-not-to-use, but the context is clear enough.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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